Daily AI Briefing — February 25, 2026

Meta signed a deal worth up to $100B with AMD for AI chips, potentially acquiring a 10% equity stake — one of the largest AI infrastructure deals ever and a major challenge to Nvidia's dominance. This comes as US tech companies are projected to spend $660B on AI assets this year, even as datacenter construction faces growing delays from energy shortages, tariffs, and community opposition.

• Anthropic released Claude Sonnet 4.6 with a 1M-token context window (4x prior), just 12 days after Opus 4.6, maintaining a breakneck release cadence. Anthropic also revealed industrial-scale distillation attacks by DeepSeek, Moonshot, and MiniMax — over 16 million exchanges across 24,000 fake accounts to extract Claude's capabilities.
• Alibaba's Qwen team launched the Qwen 3.5 Medium series, where a 35B parameter model surpasses a 235B model, demonstrating major efficiency gains in open-source AI.
• Anthropic's Claude Code for COBOL modernization triggered IBM's worst stock drop in 25 years (down 13%), showing AI's concrete disruption of enterprise consulting.
• Google DeepMind used AlphaEvolve to automatically discover novel multi-agent RL algorithms, advancing AI-driven AI research. OpenAI expanded enterprise reach through consulting firm partnerships.

Today's research spans autonomous mathematical discovery, fundamental architecture theory, AI safety evaluation, and governance analysis.

• Aletheia (Google DeepMind) autonomously solves 6/10 FirstProof challenge problems using Gemini 3 Deep Think, marking a major milestone in AI-driven mathematical research with full transparency into its reasoning process.
• Test-Time Training with KV Binding is proven equivalent to a form of learned linear attention, fundamentally unifying two active architecture research directions and overturning memorization-based interpretations of TTT.
• Some Simple Economics of AGI models the AGI transition as exponentially decaying automation costs colliding with biologically bottlenecked verification costs, offering a novel 'Cost to Supervise' framework.
• Large-Scale Online Deanonymization demonstrates LLM agents can identify anonymous users from tens of thousands of candidates across platforms, revealing critical privacy risks at unprecedented scale.

Safety and evaluation integrity feature prominently:

• Counterfactual Simulation Training improves Chain-of-Thought faithfulness by rewarding reasoning traces that actually drive model outputs, addressing a core alignment concern.
• Holden Karnofsky's analysis of Anthropic's Responsible Scaling Policy v3.0 details the shift from hard capability thresholds to continuous risk assessment.
• Prompt interference theory explains why optimizing Pass@k degrades Pass@1, with direct implications for RL post-training practice.
• First circuit tracing framework for vision-language models extends mechanistic interpretability to multimodal architectures using transcoders and attribution graphs.
• A new untrusted monitoring safety case framework relaxes prior collusion assumptions, creating a practical taxonomy for deploying AI monitors.
• An audit of SWE-Bench Pro finds widespread test leniency and quality issues, challenging a benchmark central to coding agent evaluation claims.

The AI community was dominated by several major threads on February 24th. Andrej Karpathy went massively viral arguing CLIs are ideal for AI agents, urging companies to build agent-accessible surfaces as a "legacy" technology renaissance.

• NVIDIA's Jim Fan unveiled SONIC, a 42M-parameter transformer for humanoid robot whole-body control with zero-shot sim-to-real transfer — a significant open-source robotics milestone
• Anthropic released RSP v3.0, separating unilateral safety commitments from industry recommendations and pledging new transparency via Frontier Safety Roadmaps, amid a reported Pentagon ultimatum demanding military access to Claude
• François Chollet drove an influential multi-post thread arguing AI will expand demand via Jevons paradox, not eliminate engineering jobs, while also pushing back on fatalism around AI adoption
• Neel Nanda shared interpretability research showing LLMs implicitly plan ahead when writing poetry, while Yudkowsky proposed banning AI impersonation of humans worldwide
• Ethan Mollick highlighted research showing AI narrows skill gaps by 75% between education levels, and predicted human interaction will retreat to invite-only spaces as AI floods the open web